While aging may be a complex multifactorial process with no single cause or treatment, the issue whether aging can be classified as the disease is widely debated. Many strategies for extending organismal life spans have been proposed including replacing cells and organs, comprehensive strategies for repairing the accumulated damage, using hormetins to activate endogenous repair processes, modulating the aging processes through specific mutations, gene therapy and small molecule drugs. An animal's survival strongly depends on its ability to maintain homeostasis, achieved partly through intracellular and intercellular communication within and among different tissues.
Lifespan of different cells and tissues varies substantially. Although aging affects gene expression in multiple tissues, the set of genes are highly tissue specific and depend on their functions in the tissue. As the regeneration rates and associated with it gene expression patterns vary, external effectors, such as small molecules, have different effect on different tissues. As a result, gene expression tissue specific signatures could provide information for interventions that could bring the tissues, organ, or person back to a younger state without an additional adverse effects on other tissues.
Until recently, treatments and therapies for senescence reversal (aging reversal) have been rare, largely because of the complexity of the underlying mechanisms of senescence and the lack of tools for understanding and treating senescence. One example of drug development for senescence protection (rather than senescence reversal) can be seen in US 2017/0073735. Recent bioinformatics developments such as deep neural networks have opened up the possibility of developing highly-personalized senescence reversal treatments, based on gene expression of senescent tissues versus non-senescent tissues, as will be disclosed in the present invention.
Presently, none of the proposed strategies for senescence treatment provide a roadmap for rapid screening, validation and clinical deployment. No methods currently exist to predict the effects of currently available drugs on human longevity and health span in a timely manner.
Many biomarkers of aging have been proposed including telomere length, intracellular and extracellular aggregates, racemization of the amino acids and genetic instability. Gene expression and DNA methylation profiles change during aging also may be used as biomarkers of aging. Many studies analyzing transcriptomes of biopsies in a variety of diseases indicated that age and sex of the patient have significant effects on gene expression and that there are noticeable changes in gene expression with age in mice, resulting in development of mouse aging gene expression databases and in humans.
Combinations of protein-protein interaction and gene expression in both flies and humans demonstrate that aging is mainly associated with a small number of biological processes, which might preferentially attack key regulatory nodes that are important for network stability.
Work of the inventors, among others, with gene expression and epigenetics of various solid tumors provided clues that transcription profiles of cells mapped onto the signaling pathways may be used to screen for and rate the targeted drugs that regulate pathways directly and indirectly related to aging and longevity. Prior studies suggest that a combination of pathways, termed pathway cloud, instead of one element of the pathway or the whole pathway might be responsible for pathological changes in the cell.
The senescence response causes striking changes in cellular phenotype. Aging/senescence in humans causes striking changes in cellular phenotype. According to (Campisi and d'Adda di Fagagna 2007) the senescent phenotype is induced by multiple stimuli. Mitotically competent cells respond to various stressors by undergoing cellular senescence. These stressors include dysfunctional telomeres, non-telomeric DNA damage, excessive mitogenic signals including those produced by oncogenes (which also cause DNA damage), non-genotoxic stress such as perturbations to chromatin organization and, probably, stresses with an as-yet unknown etiology. These changes include an essentially permanent arrest of cell proliferation, development of resistance to apoptosis (the death of some cells that occurs as a normal and controlled part of an organism's growth or development) and an altered pattern of gene expression. Also, the expression or appearance of senescence-associated markers such as senescence-associated β-galactosidase, p16, senescence-associated DNA-damage foci (SDFs) and senescence-associated heterochromatin foci (SAHFs) are neither universal nor exclusive to the senescent state.
Cellular senescence is thought to contribute to age-related tissue and organ dysfunction and various chronic age-related diseases through various mechanisms. Senescence is characterized by a persistent proliferative arrest in which cells display a distinct pro-inflammatory senescent-associated secretory phenotype (SASP) (Krimpenfort and Berns 2017). Whereas SASP exerts a supportive paracrine function during early development and wound healing (Demaria et al. 2014), the continuous secretion of these SASP factors has detrimental effects on normal tissue homeostasis and is considered to significantly contribute to aging (DiLoreto and Murphy 2015).
In a cell-autonomous manner, senescence acts to deplete the various pools of cycling cells in an organism, including stem and progenitor cells. In this way, senescence interferes with tissue homeostasis and regeneration, and lays the groundwork for its cell-non-autonomous detrimental actions involving the SASP. There are at least five distinct paracrine mechanisms by which senescent cells are thought to promote tissue dysfunction, including perturbation of the stem cell niche (causing stem cell dysfunction), disruption of extracellular matrix, induction of aberrant cell differentiation (both creating abnormal tissue architecture), stimulation of sterile tissue inflammation, and induction of senescence in neighboring cells (paracrine senescence). An emerging yet untested concept is that post-mitotic, terminally differentiated cells that develop key properties of senescent cells might contribute to ageing and age-related disease through the same set of paracrine mechanisms (van Deursen 2014).
Several recent observations support the hypothesis that senescence is a highly-dynamic, multi-step process, during which the properties of senescent cells continuously evolve and diversify, much like tumorigenesis but without cell proliferation as a driver (De Cecco et al. 2013; Wang et al. 2011; Ivanov et al. 2013). This includes not only senescent cells but also take in account pre-senescent stage. This fact also means there is an opportunity to reverse the cell to normal non-senescent behavior.
There has always been a need to reverse senescence, but only recently are there the necessary tools, particularly, developments in informatics and machine learning, to develop and apply such senescence therapies and treatments. Further, even commonly-accepted biomarkers and metric of such biomarkers to assess aging have been lacking.
At least two general concepts of age exist in the art. One, “chronological age” is simply the actual calendar time an organism or human has been alive. Another one, called “biological age” or “physiological age”, which is a particular focus of the present invention, is related to the physiological health of the individual, and biomarkers thereof. Biological age is associated with how well organs and regulatory systems of the body are performing and at what extent the general homeostasis at all levels of the organism is being maintained, as such functions generally decline with time and age.
The measurement of any physiological process of an organism is typically done with a set of predefined biomarkers. A biomarker can be defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. Biomarkers are chosen by scientists in order to measure a very-well defined process within the body.
Given that in a multi-cellular organism that aging is a systemic process, which cannot be readily captured by single uni-dimensional or even several metrics, the development of an accurate and useful measure of biological age (which can be thought of as a biological clock), is subject to specific challenges. Again, such biomarkers must not only be an objective quantifiable and easily measurable characteristics of the biological aging process, but must also be able to take into account that aging is not a single specific process, but rather a suite of changes across multiple physiological systems.
In other words, no single biomarker can provide an accurate overall biological clock age of a multi-cellular organism, nor can the biological age of a single cell, tissue, or organ, even when composed of many biomarkers, provide an accurate overall biological age of an organism. And in fact, it is often useful to have several biological clocks assigned to an organism or human, that is, a different biological age can be assigned to different cells, tissues, or organs of that organism, as well as different clocks based on a different biomarker or different biomarker. Thus there may be one clock for the skin, one for the liver, one clock based on telomere length of a cell(s), tissue(s), or organ(s), and another based on a different biomarker.
In the past, several attempts have been made to develop adapted biomarkers for measuring biological aging. However, the biomarkers used so far focus on monitoring a restricted number of processes known for being directly involved in the onset and propagation of aging related damages through the body. Examples of such biomarkers are telomere length (Lehmann, 2013), intracellular and extracellular aggregates, racemization of the amino acids and genetic instability. Both gene expression (Wolters, 2013) and DNA methylation profiles (Horvath, 2012, Horvath, 2013, Mendelsohn, 2013) change during aging and may be used as biomarkers of aging as demonstrated previously with the epigenetic clock (Horvath, 2012, Horvath, 2013). Many studies analyzing transcriptomes of biopsies in a variety of diseases indicated that age and sex of the patient had significant effects on gene expression (Chowers, 2003) and that there are noticeable changes in gene expression with age in mice (Weindruch, 2002, Park, 2009), resulting in development of mouse aging gene expression databases (Zahn, 2007) and in humans (Blalock, 2003; Welle, 2003; Park, 2005; Hong, 2008; de Magalhães, J. P, 2009).
The elements in the figures are arranged in accordance with at least one of the embodiments described herein, and which arrangement may be modified in accordance with the disclosure provided herein by one of ordinary skill in the art.